Egyptian Journal of Remote Sensing and Space Sciences最新文献

Dry upland agriculture is vital for securing food production in several countries. However, the research on evaluating cropping patterns using remote sensing techniques is completely neglected due to several factors such as the availability of clean imagery and the complexity of the landscape. This research primarily focused on the evaluation of data availability from three different satellite imageries: Sentinel-2, Landsat-8, and MODIS. The consistently high data availability demonstrated by Sentinel-2 established its potential as a reliable source for gap-filling analysis in remote sensing studies. Using a classification model, various land cover types were identified with an overall accuracy of 86.4%, indicating the model's efficiency in accurately classifying these areas. This research also analyzed the detailed cropping patterns, revealing seven distinct temporal cultivation patterns of various crops. This period is strategically positioned between the cultivation of maize, which spans an area of 5,943 ha in December, January, and February, suggesting a potential crop rotation system. The rotation indicated that nearly 83.7% of the cultivated land was planted between maize and shallot throughout the year. The study emphasizes the significance of continuous monitoring and adaptive management in agriculture to ensure sustainability and productivity.

The atmospheric correction of hyperspectral data stands as a fundamental step in quantitative applications, crucial for the accurate analysis of hyperspectral information. Zhuhai-1 hyperspectral data, characterized by its high spatial and spectral resolution, holds substantial potential and advantages for the quantification of water body information. Nonetheless, the adoption of more precise physical models for atmospheric correction often demands extensive satellite and ground environmental parameters, which pose practical challenges in applying physical models The Dark Object Subtraction (DOS), leveraging the intrinsic spectral characteristics of the imagery, offers an efficient alternative for achieving improved atmospheric correction results tailored to the data and study area. In this context, this study presents a Dark Object Subtraction for Water body information extraction (DOSW), specifically designed to advance the quantification of water body information in Zhuhai-1 hyperspectral data. The proposed method is rigorously evaluated by comparing the correction results from the Foshan region and Feilaixia Reservoir with standard and measured spectra of typical objects. The results demonstrate the accuracy of DOSW in atmospheric correction, with correlation coefficients exceeding 0.7 when compared to standard spectra for three representative objects. Notably, DOSW achieves exceptional accuracy in water body correction, achieving a correlation coefficient of 0.95 and an RMSE of 0.002 in the Feilaixia Reservoir, and a correlation coefficient of 0.72 and an RMSE of 0.005 in the Foshan region. Overall, the results underscore the efficacy of DOSW in accurately addressing atmospheric correction challenges to Zhuhai-1 hyperspectral data, effectively meeting the requirements of hyperspectral quantification applications.

Glaciers all over the world have been experiencing recession at varying rates. Alterations in glacial retreat, volume, and mass have direct implications for sea level rise and have implications for the overall health of glaciers. Measuring the surface ice velocity of glaciers presents challenges due to the rugged terrain and harsh climate at high altitudes, making field observations difficult. This study aims to address these challenges by focusing on the calculation of the surface ice velocity of the Durung Drung glacier in Zanskar Valley, Ladakh. Landsat- 7 & 8 panchromatic bands with a resolution of 15 m were used in conjunction with the COSI-Corr module of ENVI image processing software to derive surface ice velocity. The method involved the crosscorrelation of Landsat images from 1999/2000 and 2019/2020. The COSI-Corr module used in this study provided three key outputs: east–west displacement, north–south displacement, and signal-to-noise ratio. These outputs are essential for understanding the movement of the glacier and evaluating the quality of the image correlation. The results indicate that the surface velocity of Durung Drung glacier was 71 ± 6.1 m yr⁻¹ in 1999/2000, which increased (by ∼ 50 %) to 140 ± 7.4 m yr⁻¹ in 2019/2020. An increasing trend in velocity manifests the rising trend in temperature in the western Himalaya. Analysis of CRU TS4 gridded data reveals an increase in temperature while precipitation decreased during 1976–2020. The study recommends that the annual variations in surface ice velocity are a consequence of temperature fluctuations that influence the availability of meltwater.

Weather radar calibration is a crucial factor to be considered for quantitative applications, such as QPE (Quantitative Precipitation Estimation), which is used as input for weather risks management. The present work proposes a novel approach to the end-to-end radar calibration method through the characterization of the radar weighting functions. These are Gaussian functions that model an additional attenuation factor to the radar received power. This approach, based on the inclusion these parameters, allow the obtainment of a calibrated equivalent reflectivity factor expression for a Doppler dual-polarization weather radar that operates in the X band. To calculate these parameters, a UAS (Unmanned Aircraft System) was implemented for suspending the calibration target with a well-defined cross-section and for measuring its inclination due to wind using an IMU (Inertial Measurement Unit). From its measurements, the position of the target can be estimated, which is essential to the characterization of the weighting functions. Their inclusion within the radar equation, alongside the implementation of the angular measurement system highlight the innovation to the traditional radar calibration methodology that does not contemplate them from the explored state-of-the-art. The reflectivity was compared with the measurements from a disdrometer for a moderate rain event. An average reflectivity difference of 0.75 dBZ and a percent bias of 3.3 % were obtained between the expected and estimated measurements when including these functions compared to the 1.51 dBZ and –62.7 % obtained when disregarding them. These experimental results point out that the proposed method can deliver superior accuracy in the reflectivity estimation.

Nile Delta has been historically targeted for various types of human activities since the Pharaohs’ era due to abundance of its natural resources. The increasing land subsidence in the northern delta is a significant concern for the Egyptian government. Moreover, the cumulative subsidence coupled with the scenarios of sea level rise worsens the situation. In the present work, we utilize SNAPPING service on the Geohazards Exploitation Platform (GEP) to measure the spatial and temporal trends, as well as the extent of subsidence in urban areas within the Nile Delta. The study analyzed the period between 2015 and 2020 using 225 Copernicus Sentinel-1 radar imagery using the Persistent Scatterers Interferometry (PSI) technique and reported surface motion with an average of −5 mm/year in the Nile Delta surface. Subsidence is relatively higher in the eastern part with more patterns specific to particular regions. The northern part recorded subsidence ranged from 0 to 7 mm/year. High rates of subsidence up to 17 mm/year localized in locations of Port Said, Damietta, Ad Dakahlia and Al Sharkia governorates and around Manzala Lake. On the other side, medium uplift rates up to 6 mm/year concentrated around Bardaweel Lake, Parts of Ismailia governorate and Baltim city. On-site visual inspections in these areas documented tilting in buildings and clear cracks which confirm the impact of land subsidence on the structural integrity of the buildings. The GEP platform and the SNAPPING service demonstrated the capability of cloud-based solutions to provide precise surface motion information within a short time.

The Sustainable Development Goals (SDGs) provide a policy-making baseline for countries to overcome shortcomings and barriers for people and the planet Earth by 2030. Remote sensing (RS) enables evidence-based policy making and can contribute to realization of the SDGs by monitoring the indicators and evaluating the targets related to human and physical geography. This study exploited the RS research concerning the SDGs based on a Web of Science Core Collection database query [TS=((“remote sensing” OR “Earth observation*”) AND (“Sustainable Development Goal*”))] between 2016 and 2022 and by utilizing an artificial intelligence tool developed for SDG classification. We retrieved and analyzed articles (n = 308) using science mapping techniques. Remote Sensing is the most relevant journal publishing articles related to this theme. While the dominance of Chinese institutes in terms of authors' affiliation is clear, the highest collaboration network is between the USA and China. Our findings revealed that subjects related to carbon storage, ecological quality and impervious surface draw attention of researchers increasingly and becoming trend topics. From the SDG classification results, SDG 15 and SDG 11 emerged as the most prevalent subjects related to the RS research. Given the exponential increase in the number of studies, we recommend to employ bibliometric analysis and science mapping tools to systematically identify research patterns and gaps in both fields, as manual efforts may progressively become challenging.

This study focuses on high-temporal-resolution Vertical Total Electron Content (VTEC) estimation over Nigeria, which is crucial for enhancing satellite-based applications. Utilizing RINEX, IONEX, and SP3 data from 2011 across 10 stations, the research integrates a novel VTEC model (LIMS) based on orthogonal transformation, achieving an unprecedented 10-minute temporal resolution sampling. The model incorporates multi-Global Navigation Satellite Systems (GNSS) constellations. Geomagnetic and solar activity impact assessments involve the Ap index, sunspot number, and DSt index. Specifically, the DSt index for March 16–18, 2015, analyzes the geomagnetic storm of St Patrick’s Day. Validation compares LIMS with International GNSS Service (IGS), Center for Orbit Determination in Europe (CODE), and International Reference Ionosphere (IRI-2020) estimates, showing strong correlations during various conditions. Daily VTEC patterns reveal the lowest values in the early morning, a midday peak, occasional double peaks, secondary maximum, and post-sunset enhancements, especially during equinoxes. Seasonal analysis highlights the highest mean VTEC in September Equinox and December Solstice, and the lowest during June Solstice. Spectral analysis identifies prominent diurnal, semi-diurnal, and sub-diurnal frequency components. This research significantly advances the understanding of VTEC in Nigeria, offering a valuable tool for precise positioning, satellite communication, and space weather forecasting. Notably, 9 stations processed 2011 data, while one station from this group and an additional station were used for a 3-day storm analysis in 2015 due to data availability.

An investigation was conducted on the Khorramrood River in Iran to evaluate pollution levels resulting from human activities. Water samples were collected from eleven stations and analyzed for four parameters: pH, temperature, dissolved oxygen (DO), and nitrate (NO3). Additionally, a biodiversity assessment of macroinvertebrates was conducted to evaluate water quality. Eleven invertebrate families from seven classes were identified, with Chironomidae and Baetidae as the predominant groups, suggesting a significant deterioration in water quality. As the first objective, water quality assessment using macroinvertebrates was done using two diversity indices (Shannon-Wiener and Simpson) and four biotic indices (ASPT, FBI, EPT, and BMWP). The results consistently indicated poor water quality in the river. These findings are consistent with the conclusions drawn from the analysis of physicochemical parameters, which is the second objective, and both confirm inadequate water quality. As a part of the last objective, to map the physicochemical parameters, three scenarios were used. They involved utilizing a transformed high-resolution PRISMA image, a traditional method with Landsat 9 images, and a fusion of Landsat 9 and PRISMA images. The first scenario produced the most accurate results (RMSE = 0.624, 0.942, 0.167, and 0.98 for DO, NO3, pH, and temperature. respectively). Mapping biodiversity indices, another part of the last objective, using the transformed pan-sharpened PRISMA image proved highly reliable. A strong correlation was observed between most indices and the DO (CR = 0.972, −0.496, −0.973, and −0.978 for Simpson, EPT, BMWP, and ASPT, respectively), indicating the significant influence of DO on the river's biological state.

A significant portion of hyperspectral image (HSI) analysis involves detecting anomalous pixels, which are indicative of interesting phenomena or objects. One of the main challenges is the presence of outlier and noisy pixels in background data due to the variety of spectral signatures in heterogeneous HSIs. This article presents an effective approach using both spectral and spatial features for anomaly detection. The median filter with an appropriate size driven by using the principal component information is used for cleaning the background. Then, the image is segmented using the watershed approach. The anomaly detection occurs based on the spatial resolution by calculating each pixel's distance from its segment via spectral angle or Euclidean distance. The proposed Watershed Anomaly Detector (WAD), employs spatial features to segment the HSI properly. It also uses spectral features within each segment to detect anomalous pixels. The WAD outperforms other methods due to its simplicity and conceptual clarity. Notably, its underlying equation offers broader applicability for HSI segmentation tasks. Experiments on three benchmark datasets show WAD achieves higher accuracy and faster execution versus state-of-the-art techniques. On average across the datasets and methods, WAD attained a 6.45% higher area under the receiver operating characteristic (ROC) curve and ran 26.95 s faster than other detectors. The WAD effectively detects anomalies in varied spectral and spatial resolutions. The results highlight the stability, robustness and computational efficiency of the proposed approach across diverse data. The simultaneous effectiveness and efficiency make WAD well-suited for near real-time anomaly detection applications.

The land use and land cover (LULC) of a watershed play an important role in controlling its hydrological processes. With the help of applying the Soil and Water Assessment Tool (SWAT), this study aims to quantify the impact of changing LULC on hydrological responses and water quality in the Big Creek Lake watershed in Mobile County, South Alabama. A number of data sources were input into the SWAT model as part of its calibration and validation, including land use and land cover (LULC), weather variables, digital elevation models (DEMs), soil properties, and measured streamflows. The total monthly streamflow increased by about 62 m³/s and the average nitrogen and phosphorus are estimated to have increased by about 3,172 kg/Ha and 892 kg/Ha per year respectively over the thirty years because of the increasing agricultural land (11,406 acres), urban development (3,350 acres), and decreasing forested areas (11,482 acres). This research could be helpful for water resource managers and planners by incorporating the results in the monitoring and planning for the future.